System and method for medical imaging

ABSTRACT

The present disclosure provides a system and method for medical imaging. The method may include obtaining a preliminary image and scanning data of a subject acquired using a scanner. The method may also include determining a regularization parameter for a regularization item of an objective function based at least in part on the scanning data, wherein the regularization parameter includes at least two of a first component characterizing quality of the scanning data, a second component characterizing the scanner, or a third component characterizing a feature of the subject. The method may further include generating an image of the subject by reconstructing the preliminary image based on the objective function.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Application No.201910335013.7, filed on Apr. 24, 2019, the contents of which are herebyincorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to systems and methods formedical imaging, and more particularly, to systems and methods for noisereduction in medical images.

BACKGROUND

Medical imaging techniques including, e.g., positron emission tomography(PET), computed tomography (CT), single-photon emission computedtomography (SPECT), etc., are widely used in clinical diagnosis and/ortreatment. In a medical imaging process of a subject, one or more imagesof the subject may be reconstructed. Noise usually appears in areconstructed image of the subject, which may influence the quality ofthe image, and in turn may bring about difficulties on diagnosisperformed based on the image. Thus, it is desirable for a system andmethod for reducing noise in medical images efficiently andconveniently.

SUMMARY

According to one aspect of the present disclosure, a system is provided.The system may include at least one storage device including a set ofinstructions, and at least one processor configured to communicate withthe at least one storage device. When executing the set of instructions,the at least one processor may be configured to direct the system toperform the following operations. The following operations may includeobtaining a preliminary image and scanning data of a subject acquiredusing a scanner; determining a regularization parameter for aregularization item of an objective function based at least in part onthe scanning data, wherein the regularization parameter includes atleast two of a first component characterizing quality of the scanningdata, a second component characterizing the scanner, or a thirdcomponent characterizing a feature of the subject; and generating animage of the subject by reconstructing the preliminary image based onthe objective function.

According to another aspect of the present disclosure, a methodimplemented on a computing device having a processor and acomputer-readable storage device is provided. The method may includeobtaining a preliminary image and scanning data of a subject acquiredusing a scanner; determining a regularization parameter for aregularization item of an objective function based at least in part onthe scanning data, wherein the regularization parameter includes atleast two of a first component characterizing quality of the scanningdata, a second component characterizing the scanner, or a thirdcomponent characterizing a feature of the subject; and generating animage of the subject by reconstructing the preliminary image based onthe objective function.

In some embodiments, the scanning data is positron emission tomography(PET) data or computed tomography (CT) data.

In some embodiments, the first component relates to a noise equivalentcounts.

In some embodiments, the noise equivalent counts relates to the PETdata.

In some embodiments, the second component relates to a plurality ofsensitivity values of a spatial sensitivity of the scanner.

In some embodiments, each of the plurality of sensitivity values relateto the PET data corresponding a point in a scanning region of thescanner.

In some embodiments, the third component relates to at least one ofattenuation information or boundary information of the subject.

In some embodiments, the attenuation information is determined based ona CT image or a magnetic resonance (MR) image of the subject.

In some embodiments, the boundary information of the subject isdetermined based on a CT image, an MR image, or the preliminary image.

In some embodiments, the regularization parameter further includes oneor more global factors that relates to at least one of the image or thesubject.

In some embodiments, one of the one or more global factors includes anoise adjustment coefficient for adjusting a noise level of the image.

In some embodiments, one of the one or more global factors includes ageneral parameter of the subject.

In some embodiments, the objective function includes at least one of atotal variation function, a quadratic function, or a Huber function.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary image systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing apparatus according to someembodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device according to some embodimentsof the present disclosure;

FIG. 4 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure;

FIG. 5 includes a flowchart illustrating an exemplary process for imagedenoising in an image reconstruction according to some embodiments ofthe present disclosure;

FIG. 6 is a schematic diagram of a regularization parameter including aplurality of components according to some embodiments of the presentdisclosure;

FIG. 7 illustrates five images of a subject reconstructed based on aconstant regularization parameter under different NEC values accordingto some embodiments of the present disclosure;

FIG. 8A illustrates a relationship between NEC and noise in twentyimages of a subject reconstructed based on a constant regularizationparameter according to some embodiments of the present disclosure;

FIG. 8B illustrates a relationship between NEC and noise in twentyimages of the subject reconstructed based on a regularization parameterincluding a component related to the NEC according to some embodimentsof the present disclosure;

FIG. 9A illustrates a first set of images of a phantom reconstructedbased on an ordered subsets expectation maximization (OSEM) algorithm;

FIG. 9B illustrates a second set of images of a phantom reconstructedbased on a regularization parameter without any component related to thespatial sensitivity of the PET scanner;

FIG. 9C illustrates a third set of images of a uniform phantomreconstructed based on a regularization parameter including a componentrelated to the spatial sensitivity of the PET scanner;

FIG. 9D illustrates a schematic diagram of noise variance curvescorresponding to different reconstruction algorithms according to someembodiments of the present disclosure;

FIGS. 10A and 10B illustrate a CT image and a PET image of the brain ofthe subject according to some embodiments of the present disclosure;

FIGS. 10C and 10D illustrate boundaries of the brain identified from theCT image and the PET image according to some embodiments of the presentdisclosure;

FIG. 11 illustrates a neighborhood of a certain size centered at aselected pixel according to some embodiments of the present disclosure;and

FIG. 12 includes a flowchart illustrating an exemplary process forgenerating a denoised image according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by anotherexpression if they may achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution on acomputing apparatus (e.g., processor 210 as illustrated in FIG. 2 ) maybe provided on a computer readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing apparatus, for execution by the computing apparatus. Softwareinstructions may be embedded in firmware, such as an erasableprogrammable read-only memory (EPROM). It will be further appreciatedthat hardware modules/units/blocks may be included of connected logiccomponents, such as gates and flip-flops, and/or can be included ofprogrammable units, such as programmable gate arrays or processors. Themodules/units/blocks or computing apparatus functionality describedherein may be implemented as software modules/units/blocks, but may berepresented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to” another unit,engine, module, or block, it may be directly on, connected or coupledto, or communicate with the other unit, engine, module, or block, or anintervening unit, engine, module, or block may be present, unless thecontext clearly indicates otherwise. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

The terminology used herein is for the purposes of describing particularexamples and embodiments only, and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include” and/or“comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof.

Provided herein are systems and methods for image denosing in aniterative reconstruction process of an image. The image may bereconstructed based on positron emission tomography (PET) data, computedtomography (CT) data, emission computed tomography (ECT) system, etc.For illustration purposes, the disclosure describes systems and methodsfor radiation therapy. The term “image” used in this disclosure mayrefer to a 2D image, a 3D image, or a 4D image. In some embodiments, theterm “image” may refer to an image of a region, e.g., a region ofinterest (ROI), of a patient. The term “region of interest” or “ROI”used in this disclosure may refer to a part of an image along a line, intwo spatial dimensions, in three spatial dimensions, or any of theproceeding as they evolve as a function of time. This is not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, a certain number of variations, changes, and/ormodifications may be deduced under the guidance of the presentdisclosure. Those variations, changes, and/or modifications do notdepart from the scope of the present disclosure.

According to an aspect of the present disclosure, scanning data and apreliminary image of a subject is acquired. Noise in the preliminaryimage may correlate with at least one of a plurality of factorsincluding, e.g., the quality of the scanning data, the characteristicsof a scanning of the subject that generates the scanning data, featuresof the subject, etc. According to some embodiments of the presentdisclosure, an objective function may be determined, based on which adenoised image of the subject may be generated in an iterativerecontrcuction process. The objective function may include aregularization parameter for controlling an intensity of aregularization item. In some embodiments, the regularization parametermay relate to two or more factors exemplified above. In this case, theplurality of factors that may influence the noise in an image may beembodimented via a single parameter, thus improving the efficiency andfeasibility of image denoising in image reconstruction.

FIG. 1 is a schematic diagram illustrating an exemplary imaging system100 according to some embodiments of the present disclosure. This isunderstood that the systems and methods for image denoising are alsoapplicable in other systems, e.g., an industrial inspection system. Thefollowing descriptions are provided, unless otherwise stated expressly,with reference to a medical imaging system for illustration purposes andnot intended to be limiting. As illustrated, the imaging system 100 mayinclude an imaging scanner 110, a processing device 120, a storagedevice 130, one or more terminals 140, and a network 150. The componentsin the imaging system 100 may be connected in various ways. Merely byway of example, as illustrated in FIG. 1 , the imaging scanner 110 maybe connected to the processing device 120 through the network 150. Asanother example, the imaging scanner 110 may be connected with theprocessing device 120 directly as indicated by the bi-directional arrowin dotted lines linking the imaging scanner 110 and the processingdevice 120. As a further example, the storage device 130 may beconnected with the processing device 120 directly (not shown in FIG. 1 )or through the network 150. As still a further example, one or moreterminal(s) 140 may be connected with the processing device 120 directly(as indicated by the bi-directional arrow in dotted lines linking theterminal(s) 140 and the processing device 120) or through the network150.

For illustration purposes, a coordinate system 101 including an x axis,a y-axis, and a z-axis is provided in FIG. 1 . The x axis and the z axisshown in FIG. 1 may be horizontal, and the y-axis may be vertical. Asillustrated, the positive x direction along the x axis may be from theright side to the left side of the imaging scanner 110 seen from thedirection facing the front of the imaging scanner 110; the positive ydirection along the y axis shown in FIG. 1 may be from the lower part tothe upper part of the imaging scanner 110; the positive z directionalong the z axis shown in FIG. 1 may refer to a direction in which thesubject is moved out of the scanning channel (or referred to as thebore) of the imaging scanner 110.

The imaging scanner 110 may scan a subject or a part thereof that islocated within its detection region, and generate scanning data relatingto the (part of) subject. In the present disclosure, the terms “subject”and “object” are used interchangeably. In some embodiments, the subjectmay include a body, a substance, or the like, or a combination thereof.In some embodiments, the subject may include a specific portion of abody, such as the head, the thorax, the abdomen, or the like, or acombination thereof, of a patient. In some embodiments, the subject mayinclude a specific organ, such as the heart, the esophagus, the trachea,the bronchus, the stomach, the gallbladder, the small intestine, thecolon, the bladder, the ureter, the uterus, the fallopian tube, etc., ora portion thereof, of a patient. The imaging scanner 110 may include apositron emission computed tomography (PET) scanner, a computedtomography (CT) scanner, a single-photon emission computed tomography(SPECT) scanner, an emission computed tomography (ECT) scanner, or thelike. In some embodiment, the imaging scanner 110 may be amulti-modality device including two or more scanners exemplified above.For example, the imaging scanner 110 may be a PET-CT scanner, a PET-MRscanner, etc.

Merely for illustration purposes, a PET-CT scanner may be provided as anexample for better understanding the imaging scanner 110, which is notintended to limit the scope of the present disclosure. The PET-CT mayinclude a gantry 111, a detecting region 112, and a bed 113. The gantry111 may support one or more radiation sources and/or detectors (notshown). A subject may be placed on the bed 113 for CT scan and/or PETscan. The PET-CT scanner may combine a CT scanner with a PET scanner.When the imaging scanner 110 performs a CT scan, a radiation source mayemit radioactive rays to the subject, and one or more detectors maydetect radiation rays from the detecting region 112. The detectedradiation rays may be used to generate CT data (also referred to as CTimaging information). The one or more detectors used in CT scan mayinclude a detector (e.g., a cesium iodide detector), a gas detector,etc.

To prepare for a PET scan, a radionuclide (also referred to as “PETtracer” or “PET tracer molecules”) may be introduced into the subject.The PET tracer may emit positrons in the detecting region 112 when itdecays. An annihilation (also referred to as “annihilation event” or“coincidence event”) may occur when a positron collides with anelectron. The annihilation may produce two photons (e.g., gammaphotons), which may travel in opposite directions. The line connectingthe detector units that detecting the two gamma photons may be definedas a “line of response (LOR).” One or more detectors set on the gantry111 may detect the annihilation events (e.g., gamma photons) emittedfrom the detecting region 112. The annihilation events emitted from thedetecting region 112 may be detected and used to generate PET data (alsoreferred to as PET imaging information). In some embodiments, the one ormore detectors used in the PET scan may be different from detectors usedin the CT scan. In some embodiments, the one or more detectors used inthe PET scan may include crystal elements and photomultiplier tubes(PMT).

The processing device 120 may process data and/or information obtainedand/or retrieve from the imaging scanner 110, the terminal(s) 140, thestorage device 130 and/or other storage devices. For example, theprocessing device 120 may obtain scanning data from the imaging scanner110, and reconstruct an image of the subject based on the scanning data.As another example, the processing device 120 may determine aregularization item for regularizing the image so as to reduce noise inthe image and a regularization parameter for controlling an intensity ofthe regularization applied on the image. In some embodiments, theprocessing device 120 may be a single server or a server group. Theserver group may be centralized or distributed. In some embodiments, theprocessing device 120 may be local or remote. For example, theprocessing device 120 may access information and/or data stored in theimaging scanner 110, the terminal(s) 140, and/or the storage device 130via the network 150. As another example, the processing device 120 maybe directly connected with the imaging scanner 110, the terminal(s) 140,and/or the storage device 130 to access stored information and/or data.In some embodiments, the processing device 120 may be implemented on acloud platform. Merely by way of example, the cloud platform may includea private cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof. In some embodiments, the processing device 120 maybe implemented on a computing apparatus 200 having one or morecomponents illustrated in FIG. 2 in the present disclosure.

The storage device 130 may store data and/or instructions. In someembodiments, the storage device 130 may store data obtained from theimaging scanner 110, the terminal(s) 140, and/or the processing device120. For example, the storage device 130 may store scanning data,signals, images, videos, algorithms, texts, instructions, program codes,etc. In some embodiments, the storage device 130 may store data and/orinstructions that the processing device 120 may execute or use toperform exemplary methods described in the present disclosure. In someembodiments, the storage device 130 may include a mass storage device, aremovable storage device, a volatile read-and-write memory, a read-onlymemory (ROM), or the like, or any combination thereof. Exemplary massstorage may include a magnetic disk, an optical disk, a solid-statedrive, etc. Exemplary removable storage may include a flash drive, afloppy disk, an optical disk, a memory card, a zip disk, a magnetictape, etc. Exemplary volatile read-and-write memories may include arandom access memory (RAM). Exemplary RAM may include a dynamic RAM(DRAM), a double date rate synchronous dynamic RAM (DDR SDRAM), a staticRAM (SRAM), a thyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM),etc. Exemplary ROM may include a mask ROM (MROM), a programmable ROM(PROM), an erasable programmable ROM (PEROM), an electrically erasableprogrammable ROM (EEPROM), a compact disk ROM (CD-ROM), and a digitalversatile disk ROM, etc. In some embodiments, the storage device 130 maybe implemented on a cloud platform. Merely by way of example, the cloudplatform may include a private cloud, a public cloud, a hybrid cloud, acommunity cloud, a distributed cloud, an inter-cloud, a multi-cloud, orthe like, or any combination thereof.

In some embodiments, the storage device 130 may be connected with thenetwork 150 to communicate with one or more components of the imagingsystem 100 (e.g., the processing device 120, the terminal(s) 140, etc.).One or more components of the imaging system 100 may access the data orinstructions stored in the storage device 130 via the network 150. Insome embodiments, the storage device 130 may be directly connected orcommunicate with one or more components of the imaging system 100 (e.g.,the processing device 120, the terminal(s) 140, etc.). In someembodiments, the storage device 130 may be part of the processing device120.

The terminal(s) 140 may include a mobile device 140-1, a tablet computer140-2, a laptop computer 140-3, or the like, or any combination thereof.In some embodiments, a terminal 140 may be used to perform one or moretasks including, e.g., at least a portion of image reconstruction,providing user data of a user (e.g., the level of obesity of a patient,the weight of the patient, the height of the patient, the age of thepatient, etc.), presentation of at least one image or relevantinformation, facilitating user interaction with one or more othercomponent of the imaging system 100, etc. In some embodiments, themobile device 140-1 may include a smart home device, a wearable device,a smart mobile device, a virtual reality device, an augmented realitydevice, or the like, or any combination thereof. In some embodiments,the smart home device may include a control device of an intelligentelectronic apparatus, a smart monitoring device, a smart television, asmart video camera, an interphone, or the like, or any combinationthereof. In some embodiments, the wearable device may include a smartbracelet, smart footgear, a pair of smart glasses, a smart helmet, asmartwatch, smart clothing, a smart backpack, a smart accessory, or thelike, or any combination thereof. In some embodiments, the smart mobiledevice may include a smartphone, a personal digital assistant (PDA), agaming device, a navigation device, a point of sale (POS) device, or thelike, or any combination thereof. In some embodiments, the virtualreality device and/or the augmented reality device may include a virtualreality helmet, a virtual reality glass, a virtual reality patch, anaugmented reality helmet, an augmented reality glass, an augmentedreality patch, or the like, or any combination thereof. For example, thevirtual reality device and/or the augmented reality device may include aGoogle Glass, an Oculus Rift, a Hololens, a Gear VR, etc. In someembodiments, the terminal(s) 140 may remotely operate the imagingscanner 110. In some embodiments, the terminal(s) 140 may operate theimaging scanner 110 via a wireless connection. In some embodiments, theterminal(s) 140 may receive information and/or instructions inputted bya user, and send the received information and/or instructions to theimaging scanner 110 or the processing device 120 via the network 150. Insome embodiments, the terminal(s) 140 may receive data and/orinformation from the processing device 120. In some embodiments, theterminal(s) 140 may be part of the processing device 120. In someembodiments, the terminal(s) 140 may be omitted.

The network 150 may include any suitable network that can facilitate theexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the imaging system 100 (e.g., theimaging scanner 110, the terminal(s) 140, the processing device 120, orthe storage device 130) may communicate information and/or data with oneor more other components of the imaging system 100 via the network 150.In some embodiments, the network 150 may be any type of wired orwireless network, or a combination thereof. The network 150 may beand/or include a public network (e.g., the Internet), a private network(e.g., a local area network (LAN), a wide area network (WAN)), etc.), awired network (e.g., an Ethernet network), a wireless network (e.g., an802.11 network, a Wi-Fi network, etc.), a cellular network (e.g., a LongTerm Evolution (LTE) network), a frame relay network, a virtual privatenetwork (“VPN”), a satellite network, a telephone network, routers,hubs, switches, server computers, and/or any combination thereof. Merelyby way of example, the network 150 may include a cable network, awireline network, a fiber-optic network, a telecommunications network,an intranet, a wireless local area network (WLAN), a metropolitan areanetwork (MAN), a public telephone switched network (PSTN), a Bluetooth™network, a ZigBee™ network, a near field communication (NFC) network, orthe like, or any combination thereof. In some embodiments, the network150 may include one or more network access points. For example, thenetwork 150 may include wired and/or wireless network access points suchas base stations and/or internet exchange points through which one ormore components of the imaging system 100 may be connected with thenetwork 150 to exchange data and/or information.

It should be noted that the above description of the imaging system 100is merely provided for the purposes of illustration, not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, components contained in the imaging system 100 may becombined or adjusted in various ways, or connected with other componentsas sub-systems, and various variations and modifications may beconducted under the teaching of the present disclosure. However, thosevariations and modifications may not depart the spirit and scope of thisdisclosure. For example, the imaging scanner 110 may be a standalonedevice external to the imaging system 100, and the imaging system 100may be connected to or in communication with the imaging scanner 110 viathe network 150. All such modifications are within the protection scopeof the present disclosure.

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing apparatus 200 on which theprocessing device 120 may be implemented according to some embodimentsof the present disclosure. As illustrated in FIG. 2 , the computingapparatus 200 may include a processor 210, storage 220, an input/output(I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (program code) andperform functions of the processing device 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, signals, datastructures, procedures, modules, and functions, which perform particularfunctions described herein. For example, the processor 210 may processdata obtained from the imaging scanner 110, the terminal(s) 140, thestorage device 130, and/or any other component of the imaging system100. Specifically, the processor 210 may process scanning data obtainedfrom the imaging scanner 110. For example, the processor 210 maygenerate an image based on the scanning data. In some embodiments, theimage may be stored in the storage device 130, the storage 220, etc. Insome embodiments, the image may be displayed on a display device by theI/O 230. In some embodiments, the processor 210 may perform instructionsobtained from the terminal(s) 140. In some embodiments, the processor210 may include one or more hardware processors, such as amicrocontroller, a microprocessor, a reduced instruction set computer(RISC), an application specific integrated circuits (ASICs), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicsprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field programmable gate array (FPGA), an advancedRISC machine (ARM), a programmable logic device (PLD), any circuit orprocessor capable of executing one or more functions, or the like, orany combinations thereof.

Merely for illustration, only one processor is described in thecomputing apparatus 200. However, it should be noted that the computingapparatus 200 in the present disclosure may also include multipleprocessors. Thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing apparatus 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing apparatus200 (e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage 220 may store data/information obtained from the imagingscanner 110, the terminal(s) 140, the storage device 130, or any othercomponent of the imaging system 100. In some embodiments, the storage220 may include a mass storage device, a removable storage device, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drive, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (PEROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage 220 may store a program for the processing device120 for reducing noise in an image.

The I/O 230 may input or output signals, data, and/or information. Insome embodiments, the I/O 230 may enable user interaction with theprocessing device 120. In some embodiments, the I/O 230 may include aninput device and an output device. Exemplary input devices may include akeyboard, a mouse, a touch screen, a microphone, or the like, or acombination thereof. Exemplary output devices may include a displaydevice, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Exemplary display devices may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), or the like, or a combination thereof.

The communication port 240 may be connected with a network (e.g., thenetwork 150) to facilitate data communications. The communication port240 may establish connections between the processing device 120 and theimaging scanner 110, the terminal(s) 140, or the storage device 130. Theconnection may be a wired connection, a wireless connection, or acombination of both that enables data transmission and reception. Thewired connection may include an electrical cable, an optical cable, atelephone wire, or the like, or any combination thereof. The wirelessconnection may include Bluetooth, Wi-Fi, WiMax, WLAN, ZigBee, mobilenetwork (e.g., 3G, 4G, 5G, etc.), or the like, or a combination thereof.In some embodiments, the communication port 240 may be a standardizedcommunication port, such as RS232, RS485, etc. In some embodiments, thecommunication port 240 may be a specially designed communication port.For example, the communication port 240 may be designed in accordancewith the digital imaging and communications in medicine (DICOM)protocol.

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device 300 according to someembodiments of the present disclosure. As illustrated in FIG. 3 , themobile device 300 may include a communication module 310, a display 320,a graphics processing unit (GPU) 330, a central processing unit (CPU)340, an I/O 350, a memory 370, and storage 390. In some embodiments, anyother suitable component, including but not limited to a system bus or acontroller (not shown), may also be included in the mobile device 300.In some embodiments, a mobile operating system 360 (e.g., iOS, Android,Windows Phone, etc.) and one or more applications 380 may be loaded intothe memory 370 from the storage 390 in order to be executed by the CPU340. The applications 380 may include a browser or any other suitablemobile apps for receiving and rendering information relating to dataprocessing or other information from the processing device 120. Userinteractions with the information stream may be achieved via the I/O 350and provided to the processing device 120 and/or other components of theimaging system 100 via the network 150.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. The hardware elements, operating systems and programminglanguages of such computers are conventional in nature, and it ispresumed that those skilled in the art are adequately familiar therewithto adapt those technologies to generate an imaging report as describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or another type of work station or terminaldevice, although a computer may also act as a server if appropriatelyprogrammed. It is believed that those skilled in the art are familiarwith the structure, programming and general operation of such computerequipment and as a result, the drawings should be self-explanatory.

FIG. 4 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure. The processingdevice 120 may include an obtaining module 410, a regularization module420, and a reconstruction module 430. One or more of the modules of theprocessing device 120 may be interconnected. The connection(s) may bewireless or wired. At least a portion of the processing device 120 maybe implemented on a computing apparatus as illustrated in FIG. 2 or amobile device as illustrated in FIG. 3 .

The obtaining module 410 may data and/or information. The obtainingmodule 410 may obtain data and/or information from the imaging scanner110, the processing device 120, the storage device 130, the terminal(s)140, or any devices or components capable of storing data via thenetwork 150. For example, the obtaining module 410 may obtain dataand/or information from a medical cloud data center (not shown) via thenetwork 150. The obtained data and/or information may include scanningdata, processed results (e.g., a preliminary image, a denoised image,etc.), user instructions, algorithms, parameters (e.g., scanningparameters of the scanner 110), program codes, information of a subject,or the like, or a combination thereof. In some embodiments, theobtaining module 410 may obtain a preliminary image and scanning data ofa subject. The preliminary image and the scanning data may be obtainedfrom the imaging scanner 110, a storage device (e.g., the storage device130, the storage 220, etc.), or any other devices or components of theimaging system 100. In some embodiments, the obtaining module 410 maytransmit the obtained data and/or information to a computing device(including, for example, the regularization module 420, thereconstruction module 430, etc.) for processing.

The regularization module 420 may determine a regularization parameterfor a regularization item of an objective function. In some embodiments,the regularization parameter for the regularization item of theobjective function may be determined based at least in part on thescanning data. The objective function may refer to a target function tobe minimized in the iterative image reconstruction process such thatnoise and/or artifacts in the image may be reduced or eliminated. Insome embodiments, the objective function may also be referred to as acost function. The regularization item may be an item that regularizesan image during an image reconstruction process. The regularization itemmay also be referred to as a penalty term. During the iterative imagereconstruction process, noise in the image may be suppressed oreliminated based on the regularization item such that an overallsmoothness of the image may be improved. In some embodiments, theregularization item may be provided as part of default settings of theimaging system 100. In some embodiments, the regularization item may bespecified by a user according to actual needs. The regularizationparameter may refer to a parameter used as a coefficient relating to theregularization item. The regularization parameter may adjust the weightof the regularization item in the objective function, therebycontrolling or adjusting a strength of regularization applied on theimage.

In some embodiments, the regularization parameter may include aplurality of components. In some embodiments, the plurality ofcomponents may include at least one component characterizing the qualityof the scanning data (referred to as “first component” for brevity), atleast one component characterizing the imaging scanner 110 (referred toas “second component” for brevity), at least one componentcharacterizing features of the subject (referred to as “third component”for brevity), and one or more additional components. Illustratively, theimaging scanner 110 may be a PET scanner. Accordingly, the scanning datamay be projection data. In this case, the first component(s) may relateto a noise equivalent counts (NEC). The second component(s) may relateto a spatial sensitivity of the PET scanner. The third component(s) mayrelate to attenuation information and/or boundary information ofanatomical structures of the subject. The plurality of components maycorrespond to the one or more factors that influence the noise leveland/or noise distribution in the image of the subject. The plurality ofcomponents or at least a part thereof may control the regularization ofthe image jointly.

The reconstruction module 430 may reconstruct an image of the subject.In some embodiments, the reconstruction module 430 may generate adenoised image of the subject by reconstructing the preliminary imagebased on the objective function. In some embodiments, the reconstructionmodule 430 may reconstruct the image in an iterative process. Theiterative image reconstruction process may terminate when a thresholdnumber or count of iterations is performed or a desired image isobtained.

It should be noted that the above descriptions of the processing device120 are provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, various modifications and changes in the forms anddetails of the application of the above method and system may occurwithout departing from the principles of the present disclosure. In someembodiments, the processing device 120 may include one or more othermodules. In some embodiments, two or more units in the processing device120 may form one module. However, those variations and modificationsalso fall within the scope of the present disclosure.

FIG. 5 includes a flowchart illustrating an exemplary process forgenerating a denoised image according to some embodiments of the presentdisclosure. In some embodiments, at least a portion of the process 500may be performed by the processing device 120 (e.g., implemented in thecomputing apparatus 200 shown in FIG. 2 , the processing deviceillustrated in FIG. 4 ). In some embodiments, at least a portion of theprocess 500 may be performed by a terminal device (e.g., the mobiledevice 300 shown in FIG. 3 ) embodying software and/or hardware.

In 510, a preliminary image and scanning data of a subject may beobtained. In some embodiments, the preliminary image and the scanningdata may be obtained by obtaining module 410.

The scanning data may be generated by the imaging scanner 110. Theimaging scanner 110 may be, for example, a PET scanner, a CT scanner, anECT scanner, a SPECT scanner, etc. Illustratively, the imaging scanner110 may be a PET scanner. When the PET scanner scans the subject (e.g.,a phantom, a patient, etc.), detectors of the PET scanner may detectannihilation events in forms of gamma phantoms from, for example, thedetecting region 112. Electric signals may be generated based on thedetected annihilation events through a photoelectric conversion. The PETscanner may process the electric signals, and generate PET data of thesubject. The PET data may also be referred to as projection data. ThePET data may be determined as the scanning data.

In some embodiments, the process 500 for generating a denoised image maybe or include an iterative image reconstruction process. The preliminaryimage may be a first image of the subject used in the iterativereconstruction process. In some embodiments, the preliminary image maybe generated by reconstructing the scanning data of the subject using areconstruction algorithm. In this process, any suitable imagereconstruction algorithms may be employed according to a type of thepreliminary image (e.g., a CT image, a PET image, an ECT image, etc.).In some embodiments, the preliminary image may be any image suitable foriterative reconstruction. In some embodiments, the preliminary image maybe an image stored in a storage device (e.g., the storage device 130,the storage 220, etc.) of the imaging system 100. In some embodiments,the preliminary image may be a default image applicable for any subjector a portion thereof. For example, an image of lungs of a human may bedefined as a default image, which may be a general image applicable forany person. The default image may be pre-stored in the imaging system100.

In 520, a regularization parameter for a regularization item of anobjective function may be determined based at least in part on thescanning data. In some embodiments, the regularization parameter may bedetermined by the regularization module 420.

The objective function may refer to a target function to be minimized inthe iterative image reconstruction process such that noise and/orartifacts in the image may be reduced or eliminated. It should be notedthat the terms “image” and “reconstructed image” are usedinterchangeably in the present disclosure. Unless otherwise statedexpressly, the “image” may also refer to a reconstructed image generatedduring the iterative image reconstruction process (e.g., an intermediateimage reconstructed in an intermediate iteration of the iterativeprocess, an ultimate image reconstructed in a last iteration of theiterative process, etc.). The iterative image reconstruction process mayinvolve a termination condition relating to the objective function. Insome embodiments, the objective function may also be referred to as acost function.

The regularization item may be an item that regularizes an image duringan image reconstruction process. The regularization item may also bereferred to as a penalty term. During the iterative image reconstructionprocess, noise in the image may be suppressed or eliminated based on theregularization item such that an overall smoothness of the image may beimproved. In some embodiments, the regularization item may be providedas part of default settings of the imaging system 100. In someembodiments, the regularization item may be specified by a useraccording to actual needs. Exemplary regularization items may include atotal variation (TV) function, a quadratic function, a Huber function,etc.

The regularization parameter may refer to a parameter used as acoefficient relating to the regularization item. The regularizationparameter may adjust the weight of the regularization item in theobjective function, thereby controlling or adjusting a strength ofregularization applied on the image. In some embodiments, one or morecomponents regarding image denoising may be determined based at least inpart on the scanning data. The one or more components may be associatedwith the quality of the scanning data, the imaging scanner 110, thesubject, a radionuclide injected into the subject, or the like, or anycombination thereof. In some embodiments, the regularization parametermay be determined based on the one or more components. More detailsregarding the regularization parameter may be described elsewhere in thepresent disclosure. See, for example, FIGS. 6A and 6B and thedescriptions thereof.

In 530, an image of the subject may be generated by reconstructing thepreliminary image based on the objective function. In some embodiments,the image (also referred to as denoised image) of the subject may begenerated by the reconstruction module 430.

The objective function configured with the regularization parameter maybe employed in the iterative image reconstruction process. Since theregularization applied on the image is associated with the scanningdata, the noise in the image may be adaptively suppressed during theimage reconstruction process, thereby improving the quality of theimage.

According to the image reconstruction described with reference to theprocess 500, a processor (e.g., the processing device 120, the processor210, or the obtaining module 410) may obtain the preliminary image andthe scanning data of the subject acquired from a scanner. Theregularization parameter of the objective function may be determinedbased at least in part on the scanning data. Since the scanning data isobtained under a particular condition (e.g., associated with the qualityof the scanning data, the imaging scanner 110, the subject, aradionuclide injected into the subject, etc.), the regularizationparameter may be adaptively configured according to the particularcondition. Thus, the quality and accuracy of the reconstructed image maybe improved.

In some embodiments, the objective function used in the iterative imagereconstruction may be expressed as Formula (1):arg min_(f) ∥Mf−y∥ ₂ +γ·U(f)  (1)where y denotes the scanning data of the subject, f denotes thereconstructed image, M denotes a system matrix, ∥Mf−y∥₂ denotes a 2-normof Mf−y, γ denotes the regularization parameter, and U (f) denotes theregularization item. Merely by way of example, the regularization itemmay be a TV function.

In some embodiments, the objective function may be minimized in theiterative image reconstruction process such that noise and/or artifactsin the image may be reduced or eliminated. In some embodiment, theobjective function may be divided into two parts including part (a) andpart (b) as expressed in Formulae (2) and (3):(a) arg min_(f) ∥Mf−y∥ ₂,  (2)(b) arg min_(f) ∥f∥ _(TV) +∥f ^((n)) −f∥ ₂,  (3)wherein f^((n)) denotes an image reconstructed in an n-th iteration. Thepart (a) in Formula (2) may be a data fidelity part, and the part (b) inFormula (3) may be a regularization part for image regularization. Thetwo parts may be minimized during the iterative image reconstructionprocess. In some embodiments, the iterative image reconstruction processmay terminate when a threshold number or count of iterations isperformed or a desired result of the first part and/or the second part(e.g., an expected image) is obtained.

In some embodiments, the solution of the part (a) may be determinedthrough an iterative algorithm, such as an ordered subsets expectationmaximization (OSEM) algorithm. The solution of the part (a) may beexpressed as Formula (4):

$\begin{matrix}{{f_{j}^{(n)} = {\frac{f_{j}^{({n - 1})}}{\sum\limits_{i_{k} \in S_{k}}M_{i_{k}j}}{\sum\limits_{k \in S}{M_{i_{k}j}\left( \frac{y_{i_{k}}}{\sum\limits_{j_{p} \in L_{i_{k}}}{M_{i_{k}j_{p}}f_{t}^{({n - 1})}}} \right)}}}},} & (4)\end{matrix}$where S_(k) denotes a k-th subset of the projection data of the subject,L_(i) _(k) denotes an i_(k)-th LOR in the k-th subset, M_(i) _(k) _(j)may be a weight characterizing contribution of a j-th pixel in the imageto the L_(i) _(k) LOR which traverses the j-th pixel, and f_(j) ^((n))may be a value of the j-th pixel in an n-th iteration.

In some embodiments, the solution of the part (b) may be determinedthrough a gradient related algorithm, such as a gradient descentalgorithm. In some embodiments, the determination of the solution of thepart (b) may be implemented in another iterative process. For example,the solution of the part (b) may be expressed as Formula (5):

$\begin{matrix}{{f^{({n,{r + 1}})} = {f^{({n,r})} - {\lambda\frac{\partial{f}_{TV}}{\partial f_{x,y,z}}}}},} & (5)\end{matrix}$where λ denotes a step size of the gradient descent algorithm, r denotesan r-th iteration of the iterative process for determining theresolution of the part (b), and x, y, z denote three axes constituting acoordinate system, for example, the coordinate system 101 as illustratedin FIG. 1 . A direction with the largest gradient descent, among the x,y, and z directions, may be determined in the coordinate systemaccording to the gradient descent algorithm. The total variation may beexpressed as Formula (6):

$\begin{matrix}{{{f}_{TV} = {\int_{u}{{❘{\nabla f}❘}{dxdy}}}},} & (6)\end{matrix}$where u denotes an image or a portion thereof that needs to bereconstructed.

In some embodiments, the Formula (6) may also be expressed in a discreteform according to Formula (7):

$\begin{matrix}{{f}_{TV} = {\sum\limits_{x,y,z}{\sqrt{\left( \frac{\partial f_{{x - 1},y,z}}{\partial x} \right)^{2} + \left( \frac{\partial f_{x,{y - 1},z}}{\partial y} \right)^{2} + \left( \frac{\partial f_{x,y,{z - 1}}}{\partial z} \right)^{2}}.}}} & (7)\end{matrix}$

In some embodiments, an approximate derivative of the total variationmay be determined according to Formulae (8)-(12):

$\begin{matrix}{{\frac{\partial{f}_{TV}}{\partial f_{x,y,z}} \approx {{G \cdot \left( {\frac{\partial f_{{x - 1},y,z}}{\partial x} + \frac{\partial_{x,{y - 1},z}}{\partial y} + \frac{\partial_{x,y,{z - 1}}}{\partial z}} \right)} - {G_{x} \cdot \frac{\partial f_{x,y,z}}{\partial x}} - {G_{y} \cdot \frac{\partial f_{x,y,z}}{\partial y}} - {G_{Z} \cdot \frac{\partial f_{x,y,z}}{\partial z}}}},} & (8)\end{matrix}$ $\begin{matrix}{{G = \left\lbrack {\varepsilon + \left( \frac{\partial f_{{x - 1},y,z}}{\partial_{x}} \right)^{2} + \left( \frac{\partial f_{x,{y - 1},z}}{\partial y} \right)^{2} + \left( \frac{\partial f_{x,y,{z - 1}}}{\partial z} \right)^{2}} \right\rbrack^{- \frac{1}{2}}},} & (9)\end{matrix}$ $\begin{matrix}{{G_{x} = \left\lbrack {\varepsilon + \left( \frac{\partial f_{x,y,z}}{\partial_{x}} \right)^{2} + \left( \frac{\partial f_{{x + 1},{y - 1},z}}{\partial y} \right)^{2} + \left( \frac{\partial f_{{x + 1},y,{z - 1}}}{\partial z} \right)^{2}} \right\rbrack^{- \frac{1}{2}}},} & (10)\end{matrix}$ $\begin{matrix}{{G_{y} = \left\lbrack {\varepsilon + \left( \frac{\partial f_{x,y,z}}{\partial y} \right)^{2} + \left( \frac{\partial f_{{x - 1},{y + 1},z}}{\partial x} \right)^{2} + \left( \frac{\partial f_{x,{y + 1},{z - 1}}}{\partial z} \right)^{2}} \right\rbrack^{- \frac{1}{2}}},} & (11)\end{matrix}$ $\begin{matrix}{G_{z} = {\left\lbrack {\varepsilon + \left( \frac{\partial f_{x,y,z}}{\partial z} \right)^{2} + \left( \frac{\partial f_{{x - 1},y,{z + 1}}}{\partial{\mathfrak{r}}} \right)^{2} + \left( \frac{\partial f_{x,{y - 1},{z + 1}}}{\partial z} \right)^{2}} \right\rbrack^{- \frac{1}{2}}.}} & (12)\end{matrix}$

In this way, the solution of the part (b) may be expressed as Formula(13):f ^((n,r+1)) ≈f ^((n,r)) −λf _(x,y,z)(3G+G _(x) +G _(y) +G _(z))+λ·G(f_(x−1,y,z) +f _(x,y−1,z) +f _(x,y,z−1))+λ·(G _(x) f _(x+1,y,z) +G _(y) f_(x,y+1,z) +G _(z) f _(x,y,z+1)).  (13)

In some embodiments, in order to avoid a negative value of the solutionof the part (b), the step size λ may be specified as Formula (14):

$\begin{matrix}{\lambda = {\frac{1}{{3G} + G_{x} + G_{y} + G_{z}}.}} & (14)\end{matrix}$

The λ expressed in Formula (14) may be incorporated in the Formula (13).Then the solution of the part (b) may be expressed as Formula (15):f ^((n,r+1)) =λ·G(f _(x−1,y,z) +f _(x,y−1,z) +f _(x,y,z−1))+λ·(G _(x) f_(x+1,y,z) +G _(y) f _(x,y+1,z) +G _(z) f _(x,y,z+1)).  (15)

Thus, the solution of the part (b) may be determined as Formula (16):

$\begin{matrix}{f^{({n,{r + 1}})} = {\frac{\begin{matrix}{f^{({n,r})} + {\beta \cdot {G\left( {f_{{x - 1},y,z} + f_{x,{y - 1},z} + f_{x,y,{z - 1}} +} \right.}}} \\\left. {{G_{x}f_{{x + 1},y,z}} + {G_{y}f_{x,{y + 1},z}} + {G_{z}f_{x,y,{z + 1}}}} \right)\end{matrix}}{1 + {\beta\left( {{3G} + G_{x} + G_{y} + G_{z}} \right)}}.}} & (16)\end{matrix}$

In this case, the parameter γ may be replaced by the parameter β, andthe regularization strength may be adjusted through the single parameterβ, which may be defined as the regularization parameter.

A noise level and/or noise distribution of the image of the subject maybe influenced by one or more factors. The one or more factors mayinclude quality of the scanning data, a hardware and/or softwareconfigurations, scanning parameters, etc. of the imaging scanner 110,one or more features of the subject (e.g., attenuation information,boundary information of anatomical structures, the level of obesity ofthe subject, the age of the subject, the weight of the subject, etc.), ascanning condition (e.g., a dose of the radionuclide administered to thesubject), etc. A noise level and/or noise distribution in the image maybe different if at least one of the one or more factors changes. Merelyby way of example, a distribution of noise in a reconstructed image mayvary if the subject and/or a working condition of the imaging scanner110 changes.

Conventionally, each of the one or more factors may correspond to aregularization item and a regularization parameter that serves as aweight of the regularization item. Noise introduced by a particularfactor may be suppressed or eliminated by specifying a regularizationitem and a regularization parameter corresponding to the particularfactor in the objective function. According to some embodiments of thepresent disclosure, a regularization item and a regularization parametermay be determined. In some embodiment, the regularization item may bespecified, for example, by a user, according to default settings of theimaging system 100, etc. In some embodiments, the regularization itemmay be selected from the TV function, the quadratic function, the Huberfunction, etc. In some embodiments, the regularization item may be anysuitable function that regularizes an image during an imagereconstruction process. The regularization parameter may be determinedby considering at least two of the one or more factors that influencethe noise level and/or noise distribution in the image. In this case,noise introduced into the image by two or more factors may be suppressedor eliminated through a single parameter.

In some embodiments, the regularization parameter may include aplurality of components. In some embodiments, the plurality ofcomponents may include at least one component characterizing the qualityof the scanning data (referred to as “first component” for brevity), atleast one component characterizing the imaging scanner 110 (referred toas “second component” for brevity), at least one componentcharacterizing features of the subject (referred to as “third component”for brevity), and one or more additional components. Illustratively, theimaging scanner 110 may be a PET scanner. Accordingly, the scanning datamay be projection data. In this case, the first component(s) may relateto a noise equivalent counts (NEC). The second component(s) may relateto a spatial sensitivity of the PET scanner. The third component(s) mayrelate to attenuation information and/or boundary information ofanatomical structures of the subject. The plurality of components maycorrespond to the one or more factors that influence the noise leveland/or noise distribution in the image of the subject. The plurality ofcomponents or at least a part thereof may control the regularization ofthe image jointly.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skill in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

FIG. 6 is a schematic diagram of a regularization parameter including aplurality of components according to some embodiments of the presentdisclosure. The following descriptions regarding the regularizationparameter are provided as exemplary embodiments in which the imagingscanner 110 is a PET scanner and the scanning data is projection data,which is merely for illustration purposes and not intended to belimiting.

As illustrated in FIG. 6 , the regularization parameter may include oneor more global components and one or more local components. The one ormore global components may relate to the entire image of the subjectuniformly. In some embodiments, the global components may include atleast one constant applied over the entire image (i.e., all the pixelsof the image). The at least one constant may vary for different imagesFor example, the at least one constant may vary for images of a samesubject reconstructed based on scanning data of different quality,images of different subjects, etc. The global components may control thestrength of the regularization over an entire image.

The local components may be applied on a portion of the image (e.g., oneor more pixels, a spatially localized region, etc.). In someembodiments, a local component may be a function of pixels (or voxels)in the image. For instance, the local component includes values each ofwhich is for one pixel (or voxel) of the image. In some embodiments, alocal component may be a function of regions in the image. For instance,the local component includes values each of which is for a regionincluding one or more pixels (or voxels) of the image. A region of animage may include one or more pixels (or voxels) of the image thatsatisfy a criterion. Exemplary criteria may include that the pixel/voxelvalue exceeds or is below a threshold, that the change of pixel/voxelvalue exceeds or is below a threshold, that pixels (or voxels) arelocated within a specific portion of the image, or the like, or acombination thereof.

In some embodiments, the one or more global components may include anoise adjustment coefficient and the at least one first componentrelated to a noise equivalent counts (NEC). The noise adjustmentcoefficient may server as a scale factor of other components (e.g., acombination of the at least one first component, the at least one secondcomponent, and the at least one third component) of the regularizationparameter. The noise adjustment coefficient may adjust the magnitude ofthe regularization parameter, and control a strength of theregularization applied to the entire image. In this way, an overallnoise level of the image may be adjusted through the noise adjustmentcoefficient.

The NEC may relate to the quality of the PET data of the subject, ascanning time of a PET scan on the subject, counts of coincidence eventsdetected during the PET scan, etc. The NEC may be an accumulation ofnoise equivalent count rates (NECR) over a period of time. The NECR mayrepresent equivalent count rates of true coincidence events of imageswith a same signal-to-noise ratio (SNR) on the condition that the PETscanner is in an ideal state. As used herein, the ideal state refers toa hypothetical state in which no error or imperfection present in thePET scanner and its operation. A total count of true coincidence events(equivalent to the NEC) may be more precise in representing the qualityof the projection data than the count rates of true coincidence events(equivalent to the NECR). The image reconstruction based on projectiondata of different quality may affect the quality of the reconstructedimage directly.

In some embodiments, the one or more local parameters may include the atleast one second component related to a spatial sensitivity of the PETscanner and the at least one third component related to attenuationinformation and/or boundary information of anatomical structures of thesubject. The spatial sensitivity of the PET scanner may be representedby a plurality of sensitivity values. Each of the plurality ofsensitivity values may correspond to a point (e.g., simplified as a unitvolume or a voxel) in the scanning region of the PET scanner. In someembodiments, the scanning region of the PET scanner may be the detectingregion 112 or a portion thereof.

Detectors of the PET scanner may be set around the subject. Sincedifferent detectors have different efficiency in detecting annihilationevents (e.g., gamma photons) from the scanning region of the PETscanner, the sensitivity values at different points in the scanningregion may be different. For example, the PET scanner may have acylinder-shaped scanning region. A sensitivity value at a central pointof the scanning region of the PET scanner may be higher than asensitivity value at an end point in an axial direction of thecylinder-shaped scanning region. The spatial sensitivity of the PETscanner may affect the distribution of noise in an image reconstructedbased on data acquired by the PET scanner.

The attenuation information of the subject may relate to featuresincluding, for example, a size, a shape, a density, etc. of tissue ofthe subject. In some embodiments, the attenuation information of thesubject may be embodied in photons (e.g., gamma photons) traverseinternal structures of the subject. Photons emitted from a source mayhave different attenuation values when the photons traverse differentmedia (e.g., tissue of different types in the body of the subject). Thelarger an attenuation coefficient corresponding to a medium and/or adistance that the gamma photon passes through in the medium is, thegreater the energy loss of the gamma photon may be. Generally, a largerenergy loss of the gamma photon may result in relatively larger errorsin the projection data. Errors of the projection data with differentmagnitudes may bring about different noise levels in the image.

The boundary information of the subject may relate to anatomicalstructures of organs of the subject. During the image denoising process,sharpness of the boundaries of structures in the image may be maintainedor improved. Boundaries of structures in the image may be effectivelyutilized in identifying regions of boundaries, regions of background,regions of a specific tissue, regions of specific organs, etc., therebyimproving the accuracy of the reconstructed image. By extracting theboundaries of structures in the image, regularization of differentstrengths may be applied to different regions.

In some embodiments, the regularization parameter may be determinedaccording to the Formula (17):β=a·g(NEC)·φ(sensitivity)·ϕ(attenuation)·ψ(boundary),  (17)where β denotes the regularization parameter, a denotes the noiseadjustment coefficient, g(NEC) denotes a first component related to theNEC, φ(sensitivity) denotes a second component related to the spatialsensitivity of the PET scanner, ϕ(attenuation) denotes a third componentrelated to attenuation information of the subject, and Ψ (boundary)denotes another third component related to boundary information ofstructures of the subject.

Each of the one or more factors or a part thereof that influence thenoise in the image may be characterized by one of the above componentsthrough a particular function or algorithm. The particular function oralgorithm may represent a relationship between each of the one or morefactors or a part thereof and the noise in the image. Various ways maybe used to adjust the components. In some embodiments, it may be assumedthat the regularization intensity is proportional to a variation of thenoise.

In some embodiments, the noise adjustment coefficient may be a constantor a variable. For example, the noise adjustment coefficient may be 0.1,0.2, 0.5, 2, 5, etc. In some embodiments, the noise adjustmentcoefficient may be set by a user (e.g., a doctor, a technician, etc.),according to default settings of the imaging system 100, etc. Forexample, the noise adjustment coefficient may be an empirical-basednumber specified by a user.

In some embodiments, the NEC may be obtained by processing the scanningdata (i.e., projection data in a case that the imaging scanner 110 is aPET scanner). In some embodiments, the NEC may have a correlation withthe SNR of the image.

An example regarding the relationship between the NEC and the noise inthe image is provided for illustration purposes. FIG. 7 illustrates fiveimages of a subject reconstructed based on a constant regularizationparameter under different NEC values according to some embodiments ofthe present disclosure. The NEC of the five reconstructed imagesdecrease gradually from left to right. The NEC of the five reconstructedimages are 1.34e⁸, 6.73e⁷, 3.38e⁷, 1.66e⁷, 8.5e⁶, respectively. Arectangle box in solid lines of a white color denotes a region ofinterest in each of the five reconstructed images. As illustrated inFIG. 7 , the noise level in the ROIs increases from left to rightgradually.

FIG. 8A illustrates a relationship between NEC and noise in twentyimages of a subject reconstructed based on a constant regularizationparameter according to some embodiments of the present disclosure. Eachtriangle represents one of the twenty reconstructed images. As shown inFIG. 8A, values of noise and the NEC may follow a linear distribution. Arepresentation (e.g., a value) of noise in an image may be proportionalto the value of the NEC if the image is reconstructed based on aconstant regularization parameter.

FIG. 8B illustrates a relationship between NEC and noise in other twentyimages of the subject (the same subject as that whose images are shownin FIG. 8A) reconstructed based on a regularization parameter includinga component related to the NEC according to some embodiments of thepresent disclosure. Each quadrangle may represent one of the othertwenty reconstructed images. In comparison with FIG. 8A, the value ofnoise in an image may be substantially constant if the image isreconstructed based on a regularization parameter (e.g., theregularization parameter in Formula (21)) including a component relatedto the NEC. Thus, noise introduced into an image due to the NEC, whichmay represent the quality of the projection data, may be effectivelysuppressed if the image is reconstructed based on a regularizationparameter including a component related to the NEC.

In some embodiments, the value of the noise in the image may beexpressed as Formula (18):

$\begin{matrix}{{{Noise} = \sqrt{\frac{1}{n - 1}{\sum\limits_{i = 1}^{t}\left( {\frac{f_{i}}{\overset{\_}{f}} - 1} \right)^{2}}}},} & (18)\end{matrix}$where t denotes the number or count of pixels in a region of interest(ROI) in the image, f_(i) denotes the value of an i-th pixel in the ROI,and f denotes an average value of pixels in the ROI.

In some embodiments, the relationship between the value of noise and theNEC may be provided according to Formula (19):Noise=13.52·(NEC)^((−0.3))+0.008846  (19)where the coefficient 13.52, the negative exponent −0.3, and theconstant 0.008846 are derived based on a plurality of sample images of asubject (e.g., the images in FIGS. 8A and 8B). In some embodiments,these three values may vary in a small scale (e.g., less than 5%, 10%,or 20%) for different patients or different regions (e.g., differentorgans, tissue) of a same subject or different subjects.

In some embodiments, it may be inferred that:g(NEC)∝(NEC)^((−0.3)).  (20)

In some embodiments, the regularization parameter may be determinedaccording to Formula (21) considering the noise adjustment coefficientand the NEC:β=a·(NEC)^((−0.3)).  (21)

In some embodiments, the spatial sensitivity of the PET scanner may beobtained based on the projection data. In some embodiments, since adistribution of photons emitted from the subject may conform to aPoisson distribution, a variance of each photon corresponding todifferent spatial sensitivity values may be estimated. Merely forillustration purposes, two arbitrary photons sources (i.e., points) Aand B in the scanning region of the PET scanner are defined. Activitiesof the two sources may be λ_(A) and λ_(B). Spatial sensitivity values ofthe two sources may be S_(A) and S_(B). Expected values of theactivities of the two sources may be determined according to Formulae(22) and (23):E(A)=λ_(A),  (22)E(B)=λ_(B).  (23)

The expected values of the activities of the two sources may equal tovariance values of the activities of the two sources, respectively. Thevariance values of the activities of two gamma photons emitted in a unittime may be determined according to Formulae (24) and (25):D(S _(A) ·A)=E(S _(A) ·A)=S _(A)·λ_(A),  (24)D(S _(B) ·B)=E(S _(B) ·B)=S _(B)·λ_(B).  (25)

According to the definition of noise in Formula (18), it may be inferredthat:

$\begin{matrix}{{{{Noise}(A)} = {\frac{\sqrt{D(A)}}{\lambda_{A}} = \frac{1}{\sqrt{S_{A} \cdot \lambda_{A}}}}},} & (26)\end{matrix}$ $\begin{matrix}{{{Noise}(B)} = {\frac{\sqrt{D(B)}}{\lambda_{B}} = {\frac{1}{\sqrt{S_{B} \cdot \lambda_{B}}}.}}} & (27)\end{matrix}$

As for the two sources in the scanning region of the PET scanner, arelationship between the noise at the two sources may be determinedaccording to Formula (28):

$\begin{matrix}{{\frac{N(A)}{N(B)} = \frac{\sqrt{S_{B} \cdot \lambda_{B}}}{\sqrt{S_{A} \cdot \lambda_{A}}}},} & (28)\end{matrix}$

where N(A) may be a simplified expression of Noise (A), and N(B) may bea simplified expression of Noise(B). The noise distribution and thespatial sensitivity of the PET scanner may have a relationship expressedas:

$\begin{matrix}{N \propto {\frac{1}{\sqrt{S \cdot \lambda}}.}} & (29)\end{matrix}$

In a specific applications, the activity of a source may be omitted inthe relationship expressed as Formula (29). Thus, the second componentrelated to the spatial sensitivity of the PET scanner may be:

$\begin{matrix}{{\varphi({sensitivity})} \propto {\frac{1}{\sqrt{S}}.}} & (30)\end{matrix}$

In some embodiments, the regularization parameter may be determinedaccording to Formula (31) considering the noise adjustment coefficient,the NEC, and the spatial sensitivity of the PET scanner:

$\begin{matrix}{{\beta_{j} = {a \cdot ({NEC})^{({- 0.3})} \cdot \frac{1}{\sqrt{S_{j}}}}},} & (31)\end{matrix}$where β_(j) denotes the regularization parameter at a j-th point in theimage, and S_(j) denotes a sensitivity value at a corresponding j-thpoint in the scanning region of the PET scanner.

To better illustrate the effect of the regularization parameterincluding a component related to the spatial sensitivity of the PETscanner, a phantom made of a uniform material may be scanned by the PETscanner. In the following example, the phantom may be a cylindricalphantom.

FIG. 9A illustrates a first set of images of a phantom reconstructedbased on an ordered subsets expectation maximization (OSEM) algorithm.The OSEM algorithm may also be an iterative algorithm. The first set ofimages of the phantom may include a cross-sectional image 911, a coronalimage 912, and a sagittal image 913. A cylindrical ROI may be specifiedin the phantom (e.g., as illustrated by a rectangle box in white solidlines in each of the first set of images). As illustrated in FIG. 9A, arelatively high noise level appears as background noise in the ROI.

FIG. 9B illustrates a second set of images of a phantom reconstructedbased on a regularization parameter without any component related to thespatial sensitivity of the PET scanner. The second set of images of thephantom may include a cross-sectional image 921, a coronal image 922,and a sagittal image 923. An ROI of a same shape and position as the ROIin FIG. 9A may be defined in the phantom (not shown in the figure). Asillustrated in FIG. 9B, in a length direction (i.e., the direction 914as illustrated in FIG. 9A) of the phantom, a middle part of the ROI mayhave a relatively low noise level and an end part of the ROI may have ahigher noise level.

FIG. 9C illustrates a third set of images of a uniform phantomreconstructed based on a regularization parameter including a componentrelated to the spatial sensitivity of the PET scanner. The third set ofimages of the phantom may include a cross-sectional image 931, a coronalimage 932, and a sagittal image 933. An ROI of a same shape and positionas the ROIs in FIGS. 9A and 9B may be defined in the phantom (not shownin the figure). As illustrated in FIG. 9C, the noise level in the ROI issubstantially uniform and relatively low compared to the noise level inthe ROI of FIG. 9A.

In some embodiments, a plurality of layers may be determined in thelength direction of the uniform phantom. The number or count of theplurality of layers may be set by a user, according to default settingsof the imaging system 100, etc. Values of noise in images of theplurality of layers reconstructed based on the OSEM algorithm, theregularization parameter without any component related to the spatialsensitivity of the PET scanner, and/or the regularization parameterincluding a component related to the spatial sensitivity of the PETscanner, respectively may be quantitatively analyzed.

FIG. 9D illustrates a schematic diagram of noise variance curvescorresponding to different reconstruction algorithms according to someembodiments of the present disclosure. A noise variance curve mayindicate the variation of noise among images of different layersreconstructed based on a particular algorithm. A horizontal axis of thenoise variance curve may be a sequence of layers in the length directionof the phantom. A vertical axis of the noise variance curve may be thevalue of noise. As illustrated in FIG. 9D, a first curve 960 is a noisevariance curve corresponding to the OSEM algorithm, a second curve 970is a noise variance curve corresponding to the regularization parameterwithout any component related to the spatial sensitivity of the PETscanner, and a third curve 980 is a noise variance curve correspondingto the regularization parameter including a component related to thespatial sensitivity of the PET scanner. The regularization parameterincluding the component related to the spatial sensitivity of the PETscanner (i.e., corresponding to the third curve 980) may effectivelysuppress the noise in the reconstructed image.

In some embodiments, the attenuation information of the subject may bedetermined based on one or more images that may be used to distinguishtissue of different types and/or structures of the subject. In someembodiments, the one or more images may include a CT image, a magneticresonance (MR) image, etc. In some embodiments, the CT image may begenerated by performing a CT scan on the subject using a CT scanner. Insome embodiments, the MR image may be generated by performing an MR scanon the subject using an MR scanner. The CT scan and/or the MR scan maybe performed before, after, or in parallel with the PET scan thatgenerates the projection data.

In some embodiments, the attenuation information may be represented byan attenuation factor in a certain direction. The attenuation factor inthe direction may be determined according to Formula (32):ACE=e ^(∫−μldl),  (32)where ACE denotes the attenuation factor in the certain direction, μdenotes a linear attenuation coefficient of tissue in the direction, andl denotes a thickness of the tissue in the direction.

The attenuation information may be represented or calculated throughchordal graphs. In some embodiments, the attenuation information may becombined with the spatial sensitivity of the PET scanner for simplicity.In some embodiments, the calculation of the attenuation information maybe incorporated into the process in which the spatial sensitivity of thePET scanner is determined. For example, a spatial sensitivity mapcontaining attenuation information of the subject, which includes boththe spatial sensitivity of the PET scanner and the attenuationinformation of the subject, may be determined.

In some embodiments, the regularization parameter may be determinedaccording to Formula (33) considering the noise adjustment coefficient,the NEC, the spatial sensitivity of the PET scanner, and the attenuationinformation of the subject:

$\begin{matrix}{{\beta_{j} = {a \cdot ({NEC})^{({- 0.3})} \cdot \frac{1}{\sqrt{{sns\_ map}_{j}}}}},} & (33)\end{matrix}$where β_(j) denotes the regularization parameter at the j-th point inthe image, and sns_map_(j) denotes a value of a corresponding j-th pixelin a spatial sensitivity map containing the attenuation information ofthe subject.

In some embodiments, boundary information of structures of the subjectmay be determined from one or more images of the subject. In someembodiments, the boundary information of the subject may be determinedby identifying boundaries of anatomical structures of the subject fromthe one or more images. The one or more images may include a CT image,an MR image, a PET image (e.g., the preliminary image), etc. In someembodiments, the boundaries may be identified from the one or moreimages using an image segmentation algorithm, such as a threshold-basedsegmentation algorithm, a region-based segmentation algorithm, a neuralnetwork based segmentation algorithm, etc. Illustratively, a CT imageand a PET image of the brain of the subject may be illustrated in FIGS.10A and 10B, respectively. Boundaries of the brain may be identifiedfrom the CT image and the PET image using an image segmentationalgorithm. The boundaries of the brain identified from the CT image andthe PET image may be illustrated in FIGS. 10C and 10D, respectively.

Merely by way of example, extracted boundaries of structures in a CTimage may be expressed as Formula (34):

$\begin{matrix}{{e_{j} = {❘{0.5 - {\left( {{\sum\limits_{i \in B}\left( \frac{1}{{f_{i}/f_{j}} + 1} \right)} - 0.5} \right)/N_{B}}}❘}},} & (34)\end{matrix}$where e_(j) denotes a value of a j-th pixel on a boundary in the CTimage, B denotes a neighborhood (i.e., a region centered by the j-thpixel) of the j-th pixel in the CT image, f_(i) denotes a value of ani-th pixel in the CT image, i ∈ B, and N_(B) denotes the number or countof pixels in the neighborhood.

In some embodiments, the regularization parameter may be determinedaccording to Formula (35) considering the noise adjustment coefficient,the NEC, the spatial sensitivity of the PET scanner, the attenuationinformation of the subject, and boundary information of the subject:

$\begin{matrix}{{\psi\left( e_{j} \right)} = \left\{ {\begin{matrix}{0.5\text{  }} & {e_{j} = 0} \\{e_{j}\text{  }} & {e_{j} \neq 0}\end{matrix},} \right.} & \left( {35} \right)\end{matrix}$where Ψ(e_(j)) denotes boundary information of structures of thesubject.

In some embodiments, the regularization parameter may be determinedaccording to Formula (36) considering the noise adjustment coefficient,the NEC, the spatial sensitivity of the PET scanner, and the attenuationinformation of the subject:

$\begin{matrix}{{\beta_{j} = {a \cdot ({NEC})^{({- 0.3})} \cdot \frac{1}{\sqrt{{sns\_ map}_{j}}} \cdot {\psi\left( e_{j} \right)}}},} & (36)\end{matrix}$

In some embodiments, the image reconstruction process may include aplurality of iterations. In each of the plurality of iterations, a PETimage reconstructed in a prior iteration may be updated based on theobjective function configured with the regularization parameter. In someembodiments, the boundary information of structures of the subject maybe updated based on a reconstructed PET image in each iteration. In thiscase, the regularization parameter may also be updated in the pluralityof iterations.

In some embodiments, a CT image or an MR image of the subject may beobtained. Both the boundary information of structures and theattenuation information of the subject may be determined based on the CTimage or the MR image. In some embodiments, the CT image or the MR imagemay be obtained before the image reconstruction is initiated so as tospeed up the image reconstruction process.

In some embodiments, the attenuation information of the subject may bedetermined based on the CT image or the MR image. The boundaryinformation of structures of the subject may be determined based on apreliminary image. In some embodiments, the CT image or the MR image,and the preliminary image may be obtained before the imagereconstruction is initiated so as to speed up the image reconstructionprocess.

In some embodiments, the boundary information of structures of thesubject may be determined based on the preliminary image. For each pixelin the preliminary image, neighboring pixels within a neighborhood ofthe pixel may be determined. For example, for a selected pixel in thepreliminary image, a neighborhood of a certain size (e.g., 3×3 pixels asillustrated in FIG. 11 , 5×5 pixels, etc.) centered at the selectedpixel may be determined. Pixels around the selected pixels within theneighborhood of the selected pixel may be determined as neighboringpixels. Values of the neighboring pixels may be obtained. In someembodiments, the boundary information of structures of the subject maybe determined according to a pixel value of the selected pixel, pixelvalues of the neighboring pixels in the neighborhood of the pixel, andthe number of the neighboring pixels. In some embodiments, the boundaryinformation may be determined using differences in pixel values and thenumber or count of pixels between each pixel and its correspondingneighboring pixels in the preliminary image, which may accuratelyreflect the boundaries of structures of the subject. It should be notedthat the determination of the boundary information of the subject basedon the preliminary image is merely for illustration purposes, which mayalso be applicable to the calculation of the boundary information basedon a CT image or an MR image.

It should be noted that the above description regarding theregularization parameter is merely provided for the purposes ofillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skill in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. For example, the one ormore other components of the regularization parameter may include one ormore global factors that relate to at least one of the denoised image orthe subject. The noise adjustment coefficient may be one of the globalfactors. In some embodiments, the global factors may further include ageneral parameter of the subject. Merely by way of example, the generalparameter of the subject may include the level of obesity, the age, theweight, etc. of the subject.

FIG. 12 includes a flowchart illustrating an exemplary process forgenerating a denoised image according to some embodiments of the presentdisclosure. In some embodiments, at least a portion of the process 1200may be performed by the processing device 120 (e.g., implemented in thecomputing apparatus 200 shown in FIG. 2 , the processing deviceillustrated in FIG. 4 ). In some embodiments, at least a portion of theprocess 1200 may be performed by a terminal device (e.g., the mobiledevice 300 shown in FIG. 3 ) embodying software and/or hardware.

In 1210, a preliminary image and scanning data of a subject acquiredfrom a scanner may be obtained. In some embodiments, the preliminaryimage and the scanning data may be obtained by obtaining module 410.

The scanning data may be generated by scanning the subject using thescanner. In some embodiments, the scanner may be implemented by theimaging scanner 110. The imaging scanner 110 may be, for example, a PETscanner, a CT scanner, an ECT scanner, a SPECT scanner, etc.

In some embodiments, the preliminary image may be generated byreconstructing the scanning data of the subject using a reconstructionalgorithm. Any suitable image reconstruction algorithms may be employedaccording to a type of the preliminary image (e.g., a CT image, a PETimage, an ECT image, etc.). In some embodiments, the preliminary imagemay be any image suitable for iterative reconstruction. In someembodiments, the preliminary image may be an image stored in a storagedevice (e.g., the storage device 130, the storage 220, etc.) of theimaging system 100. In some embodiments, the operation 1210 may besimilar to or the same as the operation 510 of the process 500 asillustrated in FIG. 5 .

In 1220, a plurality of components characterizing one or more factorsrelated to noise may be determined based at least in part on thescanning data. In some embodiments, the plurality of componentscharacterizing the one or more factors related to noise may bedetermined by the regularization module 420.

The one or more factors related to noise may be attributes or featuresof a condition under which the subject is scanned and/or an image of thesubject is reconstructed. The attributes or features may influence anoise level and/or noise distribution in the image. Merely forillustration purposes, the one or more factors related to noise in animage may include the quality of the scanning data, a hardware and/orsoftware configurations, scanning parameters, etc. of the imagingscanner 110, one or more features of the subject (e.g., attenuationinformation, boundary information of anatomical structures, the level ofobesity of the subject, the age of the subject, the weight of thesubject, etc.), a scanning condition (e.g., a dose of the radionuclideadministered into the subject), etc. The plurality of components maycharacterize the one or more factors. In some embodiments, the pluralityof components may include at least one component characterizing thequality of the scanning data (referred to as “first component” forbrevity), at least one component characterizing the imaging scanner 110(referred to as “second component” for brevity), at least one componentcharacterizing features of the subject (referred to as “third component”for brevity), and one or more additional components.

In some embodiments, the at least one first component may relate to anNEC. The at least one second component may relate to a spatialsensitivity of the PET scanner. The at least one third component mayrelate to attenuation information and/or boundary information ofanatomical structures of the subject. The additional components mayinclude a noise adjustment coefficient and a general parameter of thesubject. The noise adjustment coefficient may adjust an overall noiselevel of the image. The general parameter of the subject may include thelevel of obesity, the age, the weight, etc., of the subject.

In some embodiments, the at least one first component related to theNEC, the at least one second component related to spatial sensitivity ofthe PET scanner may be determined based on the scanning data. In someembodiments, a third component relating to attenuation information ofthe subject may be determined based on the scanning data, and a CT imageor an MR image. Another third component relating to boundary informationof anatomical structures of the subject may be determined based on thescanning data, and a CT image, an MR image, or a PET image (e.g., thepreliminary image).

In 1230, a regularization parameter for a regularization item of anobjective function may be determined based on at least two of theplurality of components. In some embodiments, the plurality ofcomponents characterizing the one or more factors related to noise maybe determined by the regularization module 420.

In some embodiments, the regularization parameter may be determinedbased on the plurality of components according to Formula (36). In someembodiments, the regularization parameter may be determined based on atleast two of the at least one first component, the at least one secondcomponent, or the at least one third component. For example, theregularization parameter may be determined based on the at least onefirst component and the at least one second component according toFormula (31). As another example, the regularization parameter may bedetermined based on the at least one first component and a thirdcomponent relate to boundary information of anatomical structures of thesubject according to Formula (37):β=a·(NEC)^(−0.3)·Ψ(e _(j)).  (37)

In 1240, an image of the subject may be generated by reconstructing thepreliminary image based on the objective function. In some embodiments,the image of the subject may be generated by the reconstruction module430. The objective function configured with the regularization parametermay be employed in the iterative image reconstruction process, therebyimproving the quality of the image. In some embodiments, the operation1240 may be similar to or the same as the operation 530 of the process500 as illustrated in FIG. 5 .

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C #, VB.NET, Python or the like, conventional procedural programming languages,such as the “C” programming language, Visual Basic, Fortran 2103, Perl,COBOL 2102, PHP, ABAP, dynamic programming languages such as Python,Ruby and Groovy, or other programming languages. The program code mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider) or in a cloud computing environment oroffered as a service such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, for example, aninstallation on an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A system, comprising: at least one storage deviceincluding a set of instructions; and at least one processor configuredto communicate with the at least one storage device, wherein whenexecutingthe set of instructions, the at least one processor isconfigured to direct the system to perform operations including:obtaining a preliminary image and scanning data of a subject acquiredusing a scanner; determining a regularization parameterfor aregularization item of an objective function based at least in part onthe scanning data, wherein the regularization parameter includes a firstcomponent characterizing quality of the scanning data, and at least oneof a second component characterizing the scanner, ora third componentcharacterizing a feature of the subject, wherein the scanning data ispositron emission tomography (PET) data, and the first componentrelatesto a noise equivalent counts relatingto the PET data, the noiseequivalent counts being obtained by processing the PET data; andgenerating an image of the subject by reconstructing the preliminaryimage based on the objective function, wherein the second componentrelates to a plurality of sensitivity values of a spatial sensitivity ofthe scanner, the third component relates to attenuation information andboundary information of the subject, and the attenuation information iscombined with the spatial sensitivity of the scanner via a spatialsensitivity map that includes the spatial sensitivity of the scanner andthe attenuation information of the subject.
 2. The system of claim 1,wherein each of the plurality of sensitivity values relatesto the PETdata corresponding a point in a scanning region of the scanner.
 3. Thesystem of claim 1, wherein the attenuation information is determinedbased on a CT image or a magnetic resonance (MR) image of the subject.4. The system of claim 1, wherein the boundary information of thesubject is determined based on a CT image, an MR image, or thepreliminary image.
 5. The system of claim 1, wherein the regularizationparameterfurther includes one or more global factors that relates to atleast one of the image or the subject.
 6. The system of claim 5, whereinone of the one or more global factors includes a noise adjustmentcoefficientforadjusting a noise level of the image.
 7. The system ofclaim 5, wherein one of the one or more global factors includes ageneral parameter of the subject.
 8. The system of claim 1, wherein theobjective function includesat least one of a total variation function, aquadratic function, or a Huber function.
 9. A method implemented on acomputing device having a processor and a computer-readable storagedevice, the method comprising: obtaining a preliminary image andscanning data of a subject acquired using a scanner; determining aregularization parameterfor a regularization item of an objectivefunction based at least in part on the scanning data, wherein theregularization parameterincludesa first component characterizing qualityof the scanning data, and at least one of a second componentcharacterizing the scanner, or a third component characterizing afeature of the subject, wherein the scanning data is positron emissiontomography (PET) data, and the first component relatesto a noiseequivalent counts relatingto the PET data, the noise equivalent countsbeing obtained by processing the PET data; and generating an image ofthe subject by reconstructing the preliminary image based on theobjective function, wherein the second component relatesto a pluralityof sensitivity values of a spatial sensitivityof the scanner, the thirdcomponent relatesto attenuation information and boundary information ofthe subject, and the attenuation information is combined with thespatial sensitivity of the scanner via a spatial sensitivity map thatincludes the spatial sensitivityof the scanner and the attenuationinformation of the subject.
 10. The method of claim 9, wherein theregularization parameter further includes one or more global factorsthat relates to at least one of the image or the subject.
 11. The methodof claim 10, wherein one of the one or more global factors includes anoise adjustment coefficient foradjusti ng a noise level of the image.12. The method of claim 9, wherein each of the plurality of sensitivityvalues relates to the PET data corresponding a point in a scanningregion of the scanner.
 13. The method of claim 9, wherein theattenuation information is determined based on a CT image or a magneticresonance (MR) image of the subject.
 14. The method of claim 9, whereinthe boundary information of the subject is determined based on a CTimage, an MR image, or the preliminary image.